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Database ⇄ Data warehouse

Amazon Aurora to BigQuery integration — real-time, two-way sync

Keep Amazon Aurora and BigQuery in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon Aurora and BigQuery

Connect Amazon Aurora and BigQuery with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Amazon Aurora's rows in BigQuery, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Amazon Aurora where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Amazon Aurora sync into BigQuery in real time, and result tables in BigQuery sync back into Amazon Aurora, with schema and type mapping between the two systems handled for you.

Common use cases

  • Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • Offload sync reads to Aurora reader endpoints to avoid load on the writer instance.
  • Two-way sync between Aurora application tables and a CRM so product data and account data stay consistent.

Offload heavy reads

Point analytical queries at the synced copy in BigQuery and keep Amazon Aurora focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Amazon Aurora land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in BigQuery sync into Amazon Aurora, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Amazon Aurora and BigQuery

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Amazon Aurora objects BigQuery objects
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Partitioned tables Synced like regular tables; partition columns map to target fields.
Databases Logical databases within a cluster that scope a sync connection. Clustered tables Supported; clustering is transparent to the sync.
Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. Datasets Organizational container — you pick which dataset’s tables to sync.
Tables Relational tables synced bi-directionally at row level. Projects Connection scope: the service account grants access per project.
Views Read-only query-backed sources for downstream syncs. Tables The syncable unit: only tables can be synced per the Stacksync docs.
What ships with Amazon Aurora ⇄ BigQuery

Connect Amazon Aurora and BigQuery for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon Aurora–BigQuery connection.

Real-time

Two-way sync

Changes in Amazon Aurora or BigQuery instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora or BigQuery data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Amazon Aurora or BigQuery record.

Observability

Monitoring

Track your Amazon Aurora ⇄ BigQuery sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Aurora and BigQuery.

How the Amazon Aurora and BigQuery connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide
How it works

How to connect Amazon Aurora to BigQuery — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Amazon Aurora and BigQuery with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Amazon Aurora connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Aurora and BigQuery objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Amazon Aurora ⇄ BigQuery
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Amazon Aurora BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Aurora and BigQuery integration FAQ

SECURITY

Security teams love Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for Amazon Aurora and BigQuery.

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